updates

train.py

@@ -211,6 +211,7 @@ def train():
     print('Starting training for %g epochs...' % epochs)
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         model.train()
+        model.hyps['gr'] = 1 - (1 + math.cos(min(epoch * 2, epochs) * math.pi / epochs)) / 2  # GIoU <-> 1.0 ratio
 
         # Prebias
         if prebias:
@@ -271,7 +272,7 @@ def train():
             pred = model(imgs)
 
             # Compute loss
-            loss, loss_items = compute_loss(pred, targets, model, not prebias)
+            loss, loss_items = compute_loss(pred, targets, model)
             if not torch.isfinite(loss):
                 print('WARNING: non-finite loss, ending training ', loss_items)
                 return results

utils/utils.py

@@ -363,7 +363,7 @@ class FocalLoss(nn.Module):
             return loss
 
 
-def compute_loss(p, targets, model, giou_flag=True):  # predictions, targets, model
+def compute_loss(p, targets, model):  # predictions, targets, model
     ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
     lcls, lbox, lobj = ft([0]), ft([0]), ft([0])
     tcls, tbox, indices, anchor_vec = build_targets(model, targets)
@@ -401,7 +401,7 @@ def compute_loss(p, targets, model, giou_flag=True):  # predictions, targets, mo
             pbox = torch.cat((pxy, pwh), 1)  # predicted box
             giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True)  # giou computation
             lbox += (1.0 - giou).sum() if red == 'sum' else (1.0 - giou).mean()  # giou loss
-            tobj[b, a, gj, gi] = giou.detach().clamp(0).type(tobj.dtype) if giou_flag else 1.0
+            tobj[b, a, gj, gi] = (1.0 - h['gr']) + h['gr'] * giou.detach().clamp(0).type(tobj.dtype)  # giou ratio
 
             if 'default' in arc and model.nc > 1:  # cls loss (only if multiple classes)
                 t = torch.zeros_like(ps[:, 5:])  # targets